@inproceedings{wang-etal-2026-incomplete,
title = "Incomplete In-context Learning",
author = "Wang, Wenqiang and
Yujia, Wen and
Xiao, Yan and
Chen, Zhifeng and
Zhang, Yangshijie and
Chen, Peng and
Yang, Mingbo and
Cao, Xiaochun",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1931/",
pages = "41629--41650",
ISBN = "979-8-89176-390-6",
abstract = "Existing \textit{In-context Learning (ICL)} typically assumes the retrieval dataset contains demonstrations for all output label spaces. However, in real-world scenarios, delays in dataset updates or incomplete data annotation may result in the retrieval dataset containing labeled demonstrations for only a subset of the output space. We refer to this phenomenon as an \textit{incomplete retrieval dataset} and define the in-context learning under this condition as \textit{Incomplete In-context Learning (IICL)}. To address IICL, we propose \textit{Iterative Judgments and Integrated Prediction (IJIP)}, a framework with train-free and train-based variants. For classification, the iterative judgments stage of IJIP reformulates an (m)-class problem into (m) binary tasks, converting IICL into standard ICL. The integrated prediction stage of IJIP then refines results using both the input and initial predictions. We further extend IJIP to text regression and generation, and introduce lightweight variants that reduce computation and token costs. Across six LLMs, seven tasks, and eight datasets, IJIP achieves state-of-the-art results under two incompleteness settings and even outperforms standard ICL with complete labels. IJIP also supports a semi-supervised variant and can serve as a plug-and-play enhancement for existing ICL and zero-shot methods."
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<abstract>Existing In-context Learning (ICL) typically assumes the retrieval dataset contains demonstrations for all output label spaces. However, in real-world scenarios, delays in dataset updates or incomplete data annotation may result in the retrieval dataset containing labeled demonstrations for only a subset of the output space. We refer to this phenomenon as an incomplete retrieval dataset and define the in-context learning under this condition as Incomplete In-context Learning (IICL). To address IICL, we propose Iterative Judgments and Integrated Prediction (IJIP), a framework with train-free and train-based variants. For classification, the iterative judgments stage of IJIP reformulates an (m)-class problem into (m) binary tasks, converting IICL into standard ICL. The integrated prediction stage of IJIP then refines results using both the input and initial predictions. We further extend IJIP to text regression and generation, and introduce lightweight variants that reduce computation and token costs. Across six LLMs, seven tasks, and eight datasets, IJIP achieves state-of-the-art results under two incompleteness settings and even outperforms standard ICL with complete labels. IJIP also supports a semi-supervised variant and can serve as a plug-and-play enhancement for existing ICL and zero-shot methods.</abstract>
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%0 Conference Proceedings
%T Incomplete In-context Learning
%A Wang, Wenqiang
%A Yujia, Wen
%A Xiao, Yan
%A Chen, Zhifeng
%A Zhang, Yangshijie
%A Chen, Peng
%A Yang, Mingbo
%A Cao, Xiaochun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F wang-etal-2026-incomplete
%X Existing In-context Learning (ICL) typically assumes the retrieval dataset contains demonstrations for all output label spaces. However, in real-world scenarios, delays in dataset updates or incomplete data annotation may result in the retrieval dataset containing labeled demonstrations for only a subset of the output space. We refer to this phenomenon as an incomplete retrieval dataset and define the in-context learning under this condition as Incomplete In-context Learning (IICL). To address IICL, we propose Iterative Judgments and Integrated Prediction (IJIP), a framework with train-free and train-based variants. For classification, the iterative judgments stage of IJIP reformulates an (m)-class problem into (m) binary tasks, converting IICL into standard ICL. The integrated prediction stage of IJIP then refines results using both the input and initial predictions. We further extend IJIP to text regression and generation, and introduce lightweight variants that reduce computation and token costs. Across six LLMs, seven tasks, and eight datasets, IJIP achieves state-of-the-art results under two incompleteness settings and even outperforms standard ICL with complete labels. IJIP also supports a semi-supervised variant and can serve as a plug-and-play enhancement for existing ICL and zero-shot methods.
%U https://aclanthology.org/2026.acl-long.1931/
%P 41629-41650
Markdown (Informal)
[Incomplete In-context Learning](https://aclanthology.org/2026.acl-long.1931/) (Wang et al., ACL 2026)
ACL
- Wenqiang Wang, Wen Yujia, Yan Xiao, Zhifeng Chen, Yangshijie Zhang, Peng Chen, Mingbo Yang, and Xiaochun Cao. 2026. Incomplete In-context Learning. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 41629–41650, San Diego, California, United States. Association for Computational Linguistics.